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You searched for +publisher:"Vanderbilt University" +contributor:("Dr. Benoit Dawant"). Showing records 1 – 3 of 3 total matches.

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Vanderbilt University

1. Kumar, Ankur N. Quantifying in vivo motion in video sequences using image registration.

Degree: PhD, Electrical Engineering, 2014, Vanderbilt University

Image registration is a pivotal part of many medical imaging analysis systems that provide clinically relevant medical information. One fundamental problem addressed by image registration is the accounting of a subject’s motion. This dissertation broadly addresses the problem of quantifying in vivo motion in video sequences for two different applications using image registration. The first problem involves the correction of motion in in vivo time-series microscopy imaging of islets of Langerhans in mice. The second problem focuses on delivering near real-time 3D intraoperative movements of the cortical surface to a computational biomechanical model framework for the compensation of brain shift during brain tumor surgery. For the first application, a fully automatic algorithm is developed for the correction of in vivo time-series microscopy images of islets of Langerhans. The second application focuses on delivering near real-time 3D intraoperative movements of the cortical surface to a computational biomechanical model framework for the compensation of brain shift during brain tumor surgery. This dissertation demonstrates a clinical microscope-based digitization platform capable of reliably providing temporally dense 3D textured point clouds in near real-time of the FOV for the entire duration and under realistic conditions of neurosurgery. A fully automatic technique has been developed for robustly digitizing 3D points intraoperatively using an operating microscope at 1Hz. Another algorithm has been developed for tracking points on the cortical surface intraoperatively, which can potentially deliver intraoperative 3D displacements of the cortical surface at different time points during brain tumor surgery. Advisors/Committee Members: Dr. Michael Miga (committee member), Dr. Reid Thompson (committee member), Dr. Alan Peters (committee member), Dr. Bobby Bodenheimer (committee member), Dr. Dave Piston (committee member), Dr. Benoit Dawant (Committee Chair).

Subjects/Keywords: stereovision; image registration; in vivo; brain tumor surgery; image guided surgery; magnification

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kumar, A. N. (2014). Quantifying in vivo motion in video sequences using image registration. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/14690

Chicago Manual of Style (16th Edition):

Kumar, Ankur N. “Quantifying in vivo motion in video sequences using image registration.” 2014. Doctoral Dissertation, Vanderbilt University. Accessed January 18, 2021. http://hdl.handle.net/1803/14690.

MLA Handbook (7th Edition):

Kumar, Ankur N. “Quantifying in vivo motion in video sequences using image registration.” 2014. Web. 18 Jan 2021.

Vancouver:

Kumar AN. Quantifying in vivo motion in video sequences using image registration. [Internet] [Doctoral dissertation]. Vanderbilt University; 2014. [cited 2021 Jan 18]. Available from: http://hdl.handle.net/1803/14690.

Council of Science Editors:

Kumar AN. Quantifying in vivo motion in video sequences using image registration. [Doctoral Dissertation]. Vanderbilt University; 2014. Available from: http://hdl.handle.net/1803/14690


Vanderbilt University

2. Costello, Christopher John. Location Recognition Using a Very High Dimensional Feature Space.

Degree: PhD, Electrical Engineering, 2011, Vanderbilt University

This work is focused on creating an autonomous location recognition system that is capable of determining its location based on the percepts observed in the environment. This process involves segmenting the percepts in the region, segmenting the global region into local regions, developing models of the local regions based on the percepts present in that region, and recognizing both the percepts and regions. The models are based on the “dominant” percepts found in the global region, and are refined in order to define each local area. The feature space used to define the percepts is based on the hue, saturation, and value (HSV) color space quantized into a very high dimensional feature space (e.g. 10,000 dimensions). The global region is segmented into local regions using a relative perceptual difference measure between the current image and prior images. Once the local regions and global percepts have been found, the local models for each region are created and used for the location recognition process. Furthermore, a comparison of the current methods and prior methods of clustering the very high dimensional feature space are provided, as well as a comparison of the classification methods used based on this feature space. Finally, while the system moves through the environment, the percept blobs segmented are tracked and, based on their movement, defined. This involves recognizing reflections created by distant light sources, defining all other percepts with definitions ranging from actual percepts to aberrations of light, determining novel objects, and determining novel regions. Advisors/Committee Members: Dr. Nilanjan Sarkar (committee member), Dr. Kazuhiko Kawamura (committee member), Dr. Richard Alan Peters II (committee member), Dr. Benoit Dawant (committee member), Dr. Mitch Wilkes (Committee Chair).

Subjects/Keywords: Object segmentation; Object tracking; Location recognition; Computer vision; Very high dimensional feature space

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Costello, C. J. (2011). Location Recognition Using a Very High Dimensional Feature Space. (Doctoral Dissertation). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/13878

Chicago Manual of Style (16th Edition):

Costello, Christopher John. “Location Recognition Using a Very High Dimensional Feature Space.” 2011. Doctoral Dissertation, Vanderbilt University. Accessed January 18, 2021. http://hdl.handle.net/1803/13878.

MLA Handbook (7th Edition):

Costello, Christopher John. “Location Recognition Using a Very High Dimensional Feature Space.” 2011. Web. 18 Jan 2021.

Vancouver:

Costello CJ. Location Recognition Using a Very High Dimensional Feature Space. [Internet] [Doctoral dissertation]. Vanderbilt University; 2011. [cited 2021 Jan 18]. Available from: http://hdl.handle.net/1803/13878.

Council of Science Editors:

Costello CJ. Location Recognition Using a Very High Dimensional Feature Space. [Doctoral Dissertation]. Vanderbilt University; 2011. Available from: http://hdl.handle.net/1803/13878


Vanderbilt University

3. Suwanmongkol, Karlkim. SIMON: a distributed real-time system for critical care patient monitoring and event detection.

Degree: MS, Electrical Engineering, 2007, Vanderbilt University

Real-time patient monitoring is an essential task in the critical care unit. Care providers need to process a large amount of data obtained from patient monitoring devices and the hospital information system. Information overload can lead to sub-optimal decisions and therapeutic actions. SIMON is a system being developed to address these issues by acquiring and processing data from the bedside monitoring devices and the hospital information system. The initial SIMON prototype was deployed in the Coronary Care Unit of the Vanderbilt University Medical Center. Experience acquired with this system revealed the need for a change in architecture and a complete reimplementation. The revised SIMON has been designed with distribution in mind to achieve reliability, expandability, scalability, and flexibility. It is divided into three layers. The Data Layer provides the functionality to collect the information. The Task Layer implements signal evaluation functions to detect required event. The Knowledge Layer provides high-level reasoning capabilities. Each layer is subdivided into separate but communicating components. This thesis begins with an introduction to patient monitoring systems, the previous SIMON architecture and the revised SIMON architecture are then described. This is followed by a description of the Data Layer, the Task Layer, and the revised Task Layer. We conclude with a discussion on results we have obtained, the current status of the system, and future research recommendations. Advisors/Committee Members: Dr. Gabor Karsai (Committee Chair), Dr. Benoit Dawant (Committee Chair).

Subjects/Keywords: critical care patient monitoring; distributed system; real-time patient monitoring; Patient monitoring  – Data processing

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Suwanmongkol, K. (2007). SIMON: a distributed real-time system for critical care patient monitoring and event detection. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/13593

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Suwanmongkol, Karlkim. “SIMON: a distributed real-time system for critical care patient monitoring and event detection.” 2007. Thesis, Vanderbilt University. Accessed January 18, 2021. http://hdl.handle.net/1803/13593.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Suwanmongkol, Karlkim. “SIMON: a distributed real-time system for critical care patient monitoring and event detection.” 2007. Web. 18 Jan 2021.

Vancouver:

Suwanmongkol K. SIMON: a distributed real-time system for critical care patient monitoring and event detection. [Internet] [Thesis]. Vanderbilt University; 2007. [cited 2021 Jan 18]. Available from: http://hdl.handle.net/1803/13593.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Suwanmongkol K. SIMON: a distributed real-time system for critical care patient monitoring and event detection. [Thesis]. Vanderbilt University; 2007. Available from: http://hdl.handle.net/1803/13593

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.